MétaCan
Menu
Back to cohort
Record W3192020180 · doi:10.1097/scs.0000000000008023

Forehead Flap Templates for Nasal Reconstruction Digitally Developed From 2D and 3D Images

2021· article· en· W3192020180 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Craniofacial Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicReconstructive Facial Surgery Techniques
Canadian institutionsSunnybrook Health Science CentreUniversity of TorontoSunnybrook Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTemplateWorkflowForehead3d printedNasal cavity3d printerRapid prototyping

Abstract

fetched live from OpenAlex

ABSTRACT: The forehead flap is the gold standard procedure for nasal reconstruction to address a partial or complete rhinectomy. Traditionally, the three-dimensional (3D) nasal defect is manually templated intraoperatively to design the two-dimensional (2D) flap shape on intact morphology. In this clinical study, digital preoperative planning is used to template with computer-assisted design and manufacturing. Preoperative digital templates were implemented for 3 representative patients (1 in Supplementary Digital Content, http://links.lww.com/SCS/D60). This includes designs for a hemi-rhinectomy case from 3D mirroring, a partial total rhinectomy case generated from a 3D scan, and a total rhinectomy case generated from a 3D morphable model based on a prepathology 2D photo. Digital unwrapping flattened the patient's 3D nasal geometry designs to 2D skin flap shapes. Finally, the 2D designs were printed as traceable intraoperative templates at a 1:1 scale. This clinical study demonstrates the application of digital 3D preoperative templating to improve workflow for nasal reconstruction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.285
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it